Obesity and diabetes are more common in African-Americans than whites. Because free fatty acids (FFA) participate in the development of these conditions, studying race differences in the regulation of FFA and glucose by insulin is essential. Objective: The objective of the study was to determine whether race differences exist in glucose and FFA response to insulin. Design: This was a cross-sectional study. Setting: The study was conducted at a clinical research center. Participants: Thirty-four premenopausal women (17 African-Americans, 17 whites) matched for age 36 10 yr (mean sd) and body mass index (30.0 6.7 kg/m(2)). Interventions: Insulin-modified frequently sampled iv glucose tolerance tests were performed with data analyzed by separate minimal models for glucose and FFA. Main Outcome Measures: Glucose measures were insulin sensitivity index (S(I)) and acute insulin response to glucose (AIRg). FFA measures were FFA clearance rate (c(f)). Results: Body mass index was similar but fat mass was higher in African-Americans than whites (P < 0.01). Compared with whites, African-Americans had lower S(I) (3.71 1.55 vs. 5.23 2.74 10(-4) min(-1)/(microunits per milliliter) (P = 0.05) and higher AIRg (642 379 vs. 263 206 mU/liter(-1) min, P < 0.01). Adjusting for fat mass, African-Americans had higher FFA clearance, c(f) (0.13 0.06 vs. 0.08 0.05 min(-1), P < 0.01). After adjusting for AIRg, the race difference in c(f) was no longer present (P = 0.51). For all women, the relationship between c(f) and AIRg was significant (r = 0.64, P < 0.01), but the relationship between c(f) and S(I) was not (r = -0.07, P = 0.71). The same pattern persisted when the two groups were studied separately. Conclusion: African-American women were more insulin resistant than white women, yet they had greater FFA clearance. Acutely higher insulin concentrations in African-American women accounted for higher FFA clearance. We are collaborating with Dr. Miller in developing a new mathematical method for inferring insulin secretion rates from measured C-peptide plasma concentrations. Inference of the insulin secretion rate (ISR) from C-peptide measurements as a quantification of pancreatic beta-cell function is clinically important in diseases related to reduced insulin sensitivity and insulin action. ISR derived from C-peptide concentration is an example of non-parametric Bayesian model selection where a proposed ISR time course is considered to be a model'. An inferred value of inaccessible continuous variables from discrete observable data is often problematic in biology and medicine, because computationally efficient methods are required to solve high-dimensional constrained statistical inference problems. Predictions weighted by the posterior distribution can be cast as functional integrals as used in statistical field theory. Functional integrals are generally difficult to evaluate, especially for nonanalytic constraints such as positivity of the estimated parameters. We propose a computationally tractable method that uses the exact solution of an associated likelihood function as a prior for a Markov-chain Monte Carlo evaluation of the posterior for the full model. Our method demonstrates the feasibility of functional integral Bayesian model selection as a practical method for such data-driven inference, allowing the data to determine the smoothing time scale and the width of the prior on the space of models. We are collaborating with the Yanovski laboratory (NICHD) in extending our studies to the effects of Orlistat intervention in pediatric patients, and to comparative studies of different ethnic groups (Sumner, NIDDK). We are investigating the effects of beta-blockers on model parameters, testing the bf hypothesis that changes in insulin action on plasma FFA may be correlated with the efficacy of these drugs (collaboration with Beitelshees, University of Maryland). Furthermore, we are testing the bf hypothesis that the efficacy of TZDs in obese subjects is correlated with changes in parameters in the mathematical model of insulin action on lipolysis (collaboration with Snitker, University of Maryland).